基于脑电熵值特征和功能连接的不同线型道路下驾驶状态检测

常文文; 闫光辉; 杨志飞; 张冰涛; 罗浩

doi:10.12263/DZXB.20210885

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基于脑电熵值特征和功能连接的不同线型道路下驾驶状态检测

学术论文 | 更新时间：2025-12-08

- 基于脑电熵值特征和功能连接的不同线型道路下驾驶状态检测
- Detection of Driving State Under Different Curve Road based on Entropy and Functional Connectivity of EEG
- 电子学报 2023年51卷第10期页码：2874-2883
- 作者机构：
  
  兰州交通大学电子与信息工程学院，甘肃兰州 730070
- 作者简介：
  
  [ "常文文男，1987年出生，甘肃通渭人．2019年于东北大学获得工学博士学位.瑞士洛桑联邦理工学院神经义肢技术中心访问学者.现为兰州交通大学副教授、硕士生导师.主要研究方向为事脑-机交互、脑电信号处理和模式识别.中国电子学会会员编号：E190085440M.E-mail: changww2013@126.com" ]
  [ "闫光辉（通讯作者）男，1970年出生，河南睢县人.博士.现为兰州交通大学教授、博士生导师.主要研究方向为大数据智能挖掘、复杂网络分析和物联网工程与应用等." ]
- 基金信息：
  
  兰州市科技计划项目(2021-1-150);国家自然科学基金(62062049;61962034);甘肃省科技计划项目(21ZD8RA008);兰州交通大学“天佑青年托举人才计划”基金(20190024)
- DOI：10.12263/DZXB.20210885
  中图分类号： TP391;TP18
- 收稿：2021-07-12，
  
  修回：2022-04-22，
  
  纸质出版：2023-10-25
- 稿件说明：
移动端阅览
常文文,闫光辉,杨志飞等.基于脑电熵值特征和功能连接的不同线型道路下驾驶状态检测[J].电子学报,2023,51(10):2874-2883.

CHANG Wen-wen,YAN Guang-hui,YANG Zhi-fei,et al.Detection of Driving State Under Different Curve Road based on Entropy and Functional Connectivity of EEG[J].ACTA ELECTRONICA SINICA,2023,51(10):2874-2883.
常文文,闫光辉,杨志飞等.基于脑电熵值特征和功能连接的不同线型道路下驾驶状态检测[J].电子学报,2023,51(10):2874-2883. DOI： 10.12263/DZXB.20210885.

CHANG Wen-wen,YAN Guang-hui,YANG Zhi-fei,et al.Detection of Driving State Under Different Curve Road based on Entropy and Functional Connectivity of EEG[J].ACTA ELECTRONICA SINICA,2023,51(10):2874-2883. DOI： 10.12263/DZXB.20210885.

摘要

基于脑电信号完成对不同驾驶过程的解码分析，并就驾驶意图做出预测，是基于脑机接口的人机协同智能驾驶控制中的核心问题.为了实现对直线、左弯道和右弯道驾驶过程的识别，本文提出了基于脑电功能性脑网络和熵值特征的驾驶行为特征检测方法，并结合支持向量机和高斯混合模型等算法完成对不同线型驾驶过程的分类识别.模拟驾驶实验结果表明，本文提出的方法可有效实现对不同线型驾驶过程的识别，针对16名被试对直线和弯道驾驶过程的识别准确率均高于82%，最高达到86.66%，对左弯道和右弯道驾驶过程的识别准确率均高于75%，最高达到77.95%.对主要脑区间相互依赖关系的分析结果表明，弯道驾驶过程表现出明显的大脑对侧性特征，且左弯道驾驶相比右弯道需要更多的脑区间交互活动，而直线驾驶过程中左脑区的活动稍强于右脑区.本文研究结果对理解弯道驾驶过程中驾驶员脑认知特性，以及开展不同线型道路下驾驶行为检测和驾驶状态研究，具有一定的参考价值.

Abstract

The decoding of different driving processes and driving intention prediction based on electroencephalogram (EEG) signals are the key issue of human computer interface based intelligent driving control. In order to realize the identification of driving process in straight road

left curve and right curve road

this paper proposes a feature extraction method for driving behaviors based on functional brain network and entropy features of EEGs

and achieve thes classification of different driving conditions under various curve by combining with several classifiers with the extracted EEG features. Corresponding simulation driving experiments are designed and the results show that the method proposed in this study can effectively identiy the driving process under various curve. All the classification accuracy for the 16 subjects between straight and curve road are higher than 82%

with the highest value to 86.66%

while the accuracy between left curve and right curve are higher than 75%

with the highest value to 77.95%. Interdependence analysis between different brain regions shows that there are obvious brain contralateral characteristics during curve driving. In addition

left curve driving needs more interactions between the brain regions than right curve driving

while the left hemisphere is slightly more active than the right hemisphere during straight driving. The result has significant value for understanding driver's brain cognitive characteristics in the process of curve driving and driving behavior detection under different curve road.

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references

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